A Novel Simple-Based Pressure-Enthalpy Coupling Scheme for Engine Flow Problems

Abstract

A novel method in CFD derived from the SIMPLE algorithm is presented. Instead of solving the linear equations for each variable and the pressurecorrection equation separately in a so-called segregated manner, it relies on the solution of a linear system that comprises the discretisation of enthalpy and pressurecorrection equation which are linked through physical coupling terms. These coupling terms reflect a more accurate approximation of the density update with respect to thermodynamics (compared to standard SIMPLE method). We show that the novel method is a reasonable extension of existing CFD techniques for variable density flows based on SIMPLE. The novel method leads to a reduction of the number of iterations of SIMPLE which translates in many – but not in all – cases to a reduction in computing time. We will therefore demonstrate practical advantages and restrictions in terms of computational efficiency for industrial CFD applications in the field of piston engine simulations.

Data Filtering Based Stochastic Gradient Algorithms for Multivariable Carar-Like Systems

“…Multivariable system modelling has received much attention in various practical systems, including magnetic compressors and magnetic fluids [9,17], piston engines [8], distillation columns [13,14], fault detection systems [12,18] and travelling waves [11], etc. As a consequence of this wide variety of applications, different identification algorithms for multivariable systems have been vastly reported in the literature, e.g., the gradient based iterative algorithm and the least squares based iterative algorithm for multivariable CARARMA systems [7], the hierarchical gradient-based iterative identification algorithms for multivariable CARAR-like systems [21], the stochastic gradient estimation algorithm for multivariable equation error systems [15], the auxiliary modelbased multi-innovation stochastic gradient algorithm for multiple-input singleoutput systems [16], the bias compensation based identification algorithms for multivariable systems [22,23], and the coupled-least-squares identification for multivariable systems [1].…”